System Identification of a Building with Mid-story Seismic Isolation using Artificial Neural Networks
Date Issued
2014
Date
2014
Author(s)
Shih, Chi-Yang
Abstract
In the recent years, the techniques against the earthquake are used to mitigate the effects of nature hazards on civil infrastructure, especially in isolation technology, which widely used in a lot of administrative, commercial and residential buildings. It will help a lot if we can clearly get the characteristics by simplifying isolation technology. So an identification model of isolation building can be established.
The objective of the research is to establish a system identification model of building with mid-story isolation, expecting to identify meaningful and physical parameters of building with a simplified model. We use lump mass method, simplifying a building with mid-story isolation into a three lump mass system. Respectively, these are superstructure, isolated-structure and substructure. And we consider there are three degrees of freedom (longitudinal, transverse and torsional) in every lump mass, total of nine degrees of freedom. For the superstructure, we use effective modal mass method to simplify multiple degrees of freedom system, and consider torsional coupling effect with it. Bilinear mechanical behavior assumed in isolation story with LRB, following the Skeleton curve in Masing Rule.
In the research, integrating following theory and Artificial Neural Network (ANN), we analyze the relationship between the structure vibration and ground-motion and use Back Propagation Network (BPN) to build the model. We can divide it into two stages, linear and nonlinear model, in the process of development. Linear model is major to build the framework with ANN. By the framework, we can set up bilinear mechanical behavior into nonlinear model. After developing, the model will be verified by numerical analysis after developing at each stage of model development. We design the structure parameter with nine degrees of freedom of mid-story isolation building model. Next, we input El Centro earthquake and calculated dynamics response of all degrees of freedom by Newmark β linear acceleration method. Subsequently, regarding the behavior of each degree of freedom as measurement data, the identification model is executed to verify the suitability of identification theory and modified network framework.
In identification of real case, the data gathered by strong motion instrumentation program in the New Research Building of Civil Engineering department of National Taiwan University is adopted as measurement data to implement practical analysis, both the single and multi-section identification are accomplished and discussed in the research. At final, the reliability of the frame of network is verified by calculating the error index between the result of identification model and measurement data.
Subjects
系統識別
中間層隔震建築物
類神經網路
Type
thesis
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